Authors: Jie Deng, Yunxiang Li, Xiao Liang, Weiguo Lu, Jiacheng Xie, You Zhang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, University of Texas Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, The University of Texas at Dallas
Abstract Preview: Purpose: Recently, foundational models trained on large datasets have shown remarkable performance across various tasks. Developing a foundational model for medical image modality translation in head-...
Authors: Eric Chang, Nguyen Phuong Dang, Andrew Lim, Lauren Lukas, Lijun Ma, Yutaka Natsuaki, Zhengzheng Xu, Hualin Zhang
Affiliation: Radiation Oncology, Keck School of Medicine of USC
Abstract Preview: Purpose: Harnessed the power of AI and Deep Learning (DL), Generalized Neural Network models for medical image transformation are trained to predict target images from reference images, often requirin...
Authors: Hilary P Bagshaw, Mark K Buyyounouski, Cynthia Fu-Yu Chuang, Yu Gao, Dimitre Hristov, Lianli Liu, Lawrie Skinner, Lei Xing
Affiliation: Department of Radiation Oncology, Department of Radiation Oncology, Stanford University
Abstract Preview: Purpose:
MR-guided radiation therapy has introduced a significant leap in cancer treatment by allowing adaptive treatment. The low-field MR-guided system predominantly uses the TrueFISP sequence, w...
Authors: Silambarasan Anbumani, Nicolette O'Connell, Eenas A. Omari, Amanda Pan, Eric S. Paulson, Lindsay Puckett, Monica E. Shukla, Dan Thill, Jiaofeng Xu
Affiliation: Elekta Inc, Elekta Limited, Linac House, Department of Radiation Oncology, Medical College of Wisconsin
Abstract Preview: Purpose: Accurate electron density information from on-board imaging is essential for direct dose calculations in adaptive radiotherapy (ART). This study evaluates a deep learning model for thoracic s...
Authors: Jie Hu, Zhengdong Jiang, Nan Li, Tie Lv, Yuqing Xia, Shouping Xu, Gaolong Zhang, Wei Zhao, Changyou Zhong
Affiliation: School of Physics, Beihang University, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Radiotherapy Department of Meizhou People’s Hospital (Huangtang Hospital), UT Health San Antonio, National Cancer Center/ National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, Peopleʼs Republic of China, Department of Radiation Oncology
Abstract Preview: Purpose: Patients usually undergo cone-beam computed tomography (CBCT) scans which are used for patient set-up before radiotherapy. However, the low image quality of CBCT hinders its use in adaptive r...
Authors: Chuangxin Chu, Haotian Huang, Tianhao Li, Jingyu Lu, Zhenyu Yang, Fang-Fang Yin, Tianyu Zeng, Chulong Zhang, Yujia Zheng
Affiliation: The Hong Kong Polytechnic University, Nanyang Technological University, Australian National University, Medical Physics Graduate Program, Duke Kunshan University, North China University of Technology, Duke Kunshan University
Abstract Preview: Purpose: Deep learning segmentation models, such as U-Net, rely on high-quality image-segmentation pairs for accurate predictions. However, the recent increasing use of generative networks for creatin...
Authors: Jee Suk Chang, Hojin Kim, Jin Sung Kim, Jaehyun Seok
Affiliation: Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Department of Integrative Medicine
Abstract Preview: Purpose: This study aims to leverage 3D dose distribution data to develop a machine learning model capable of accurately predicting lymphedema occurrence in patients undergoing 3D conformal radiation ...
Authors: Mingli Chen, Xuejun Gu, Hao Jiang, Mahdieh Kazemimoghadam, Weiguo Lu, Qingying Wang, Kangning Zhang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Department of Radiation Oncology, Stanford University School of Medicine
Abstract Preview: Purpose:
PET is used in radiotherapy workflows for accurate target delineation. However, a separate CT scan is typically required for attenuation correction in PET imaging and for registering PET-d...
Authors: Kimberly Chan, Anke Henning, Mahrshi Jani, Andrew Wright, Xinyu Zhang
Affiliation: Advanced Imaging Research Center (AIRC), UT Southwestern Medical Center
Abstract Preview: Purpose: To evaluate the performance of multiple deep learning architectures for MRSI reconstruction and determine their effectiveness in maintaining high-resolution metabolite mapping while reducing ...
Authors: Abdusalam Abdukerim
Affiliation: Institute for Medical Imaging Technology, Ruijin Hospital
Abstract Preview: Purpose:
Coronary computed tomography angiography (CCTA) is the gold-standard non-invasive test for coronary artery disease (CAD), but iodine contrast agents (ICA) pose limitations in specific popu...
Authors: Derek Tang, Susu Yan
Affiliation: Massachusetts General Hospital
Abstract Preview: Purpose: To evaluate the performance of a multi-task automated-segmentation and synthetic CT generation model (sCT) and investigate its application in an adaptive proton therapy workflow.
Methods: ...
Authors: Shinichiro Mori, Isabella Pfeiffer, Chester R. Ramsey, Alexander Usynin
Affiliation: Thompson Proton Center, National Institutes for Quantum Science and Technology, Thompson Cancer Survival Center
Abstract Preview: Purpose: Four-dimensional CT imaging (4DCT) has become a standard tool for managing respiratory motion in radiation therapy. However, many treatment delivery systems and most diagnostic CT scanners la...
Authors: Louis Archambault, Nicolas Drouin, Alexis Horik, Simon Thibault
Affiliation: Département de Physique, de Génie Physique et D'optique, et Centre de Recherche sur le Cancer, Université Laval, Département de Physique, de Génie Physique et D'optique, et Centre d'optique, photonique et laser, Université Laval
Abstract Preview: Purpose: To develop a novel type of real-time 3D dosimeter for the quality assurance of linear accelerators used in external beam radiotherapy.
Methods: An experimental setup was constructed using ...
Authors: Weigang Hu, Zhenhao Li, Jiazhou Wang, Xiaojie Yin, Zhen Zhang
Affiliation: Fudan University Shanghai Cancer Center
Abstract Preview: Purpose:
This study aims to develop and validate a novel deep learning method to generate synthetic PET images for rectal cancer from MRI data. By incorporating metabolic information from the synth...
Authors: Sofia Beer, Menal Bhandari, Alec Block, Nader Darwish, Joseph Dingillo, Sebastien A. Gros, Hyejoo Kang, Andrew Keeler, Rajkumar Kettimuthu, Jason Patrick Luce, Ha Nguyen, John C. Roeske, George K. Thiruvathukal, Austin Yunker
Affiliation: Data Science and Learning Division, Argonne National Laboratory, Department of Radiation Oncology, Stritch School of Medicine, Loyola University Chicago, Stritch School of Medicine Loyola University Chicago, Cardinal Bernardin Cancer Center, Loyola University Chicago, Department of Computer Science, Loyola University of Chicago
Abstract Preview: Purpose: Artificial intelligence (AI) generated synthetic medical images are seeing increased use in radiology and radiation oncology. Physician observer studies are an ideal way to evaluate the usabi...
Authors: Kofi M. Deh, Tamas Jozsa, Tsang-Wei Tu
Affiliation: Cranfield University, Howard University Hospital, Howard University
Abstract Preview: Purpose: To enhance the quality of hyperpolarized (HP) 13C magnetic resonance images by integrating deep learning with perfusion modeling.
Methods: A convolutional neural network (CNN) and a superr...
Authors: Mark Ashamalla, Renee Farrell, Jinkoo Kim, Kartik Mani, Xin Qian, Samuel Ryu, Yizhou Zhao
Affiliation: Stony Brook Medicine, Stony Brook University Hospital
Abstract Preview: Purpose: Adaptive planning is increasingly used in head and neck radiation therapy due to factors like tumor response or changes in patient anatomy. However, methods such as resimulation or offline re...
Authors: Hilary P Bagshaw, Mark K Buyyounouski, Serdar Charyyev, Xianjin Dai, PhD, Yu Gao, Thomas R. Niedermayr, Lei Xing
Affiliation: Department of Radiation Oncology, Stanford University
Abstract Preview: Purpose: Real-time transrectal ultrasound imaging is the gold standard for needle placement and treatment planning of real-time based-ultrasound-based high dose-rate (HDR) prostate brachytherapy. Cumu...
Authors: Huang Chi-Shiuan, Wu Chih-Chun, Hui-Yu Cathy Tsai, Chen Yan-Han, Chen Yi-Wei, Pan Yi-Ying
Affiliation: Institute of Nuclear Engineering and Science, National Tsing Hua University, Taipei Veterans General Hospital, Tri-Service General Hospital
Abstract Preview: Purpose:
This study aims to develop and validate a machine learning (ML) model based on MRI-derived radiomic features to predict progressive disease (PD) in glioblastoma (GBM) patients four months ...
Authors: Jin Sung Kim, Chanwoong Lee, Young Hun Yoon
Affiliation: Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine
Abstract Preview: Purpose: Chest contrast-enhanced CT (CECT) serves as a valuable tool for cardiac imaging, but its lack of detailed anatomical visualization limits its utility in segmentation tasks. While CECT offers ...
Authors: Gregory Bolard, Rabten Datsang, Sarah Ghandour, Timo Kiljunen, Pauliina Paavilainen, Sami Suilamo, Katlin Tiigi
Affiliation: Turku University Hospital, Virginia Commonwealth University, MVision AI, North Estonia Medical Centre, Docrates Cancer Center, Hopital Riviera-Chablais
Abstract Preview: Purpose: To verify the performance of a vendor-neutral deep learning model for synthetic CT generation from T2-weighted and balanced steady-state MR sequences to support both MR-only simulation and MR...
Authors: Rituparna Basak, Maede Boroji, Renee F Cattell, Vahid Danesh, Imin Kao, Kartik Mani, Xin Qian, Samuel Ryu, Tiezhi Zhang
Affiliation: Stony Brook Medicine, Stony Brook University, Washington University in St. Louis, Stony Brook University Hospital
Abstract Preview: Purpose: Fundamental qualitative characteristics physicians use to differentiate skin lesion subtypes include asymmetry, border irregularity, and color. Radiomic features have potential to quantify th...
Authors: Samuel Kadoury, Redha Touati
Affiliation: Polytechnique Montréal
Abstract Preview: Purpose:
Generating synthetic CT images from MR acquisitions for radiotherapy planning allows to integrate soft tissue contrast alongside density information stemming from CT, thus improving tumor ...
Authors: Michael Baine, Charles Enke, Yang Lei, Yu Lei, Ruirui Liu, Su-Min Zhou
Affiliation: Icahn School of Medicine at Mount Sinai, University of Nebraska Medical Center, Department of Radiation Oncology, University of Nebraska Medical Center
Abstract Preview: Purpose: This study presents a framework for generating synthetic CT images using a Cycle Diffusion model, which can be utilized to enhance needle conspicuity in ultrasound-guided prostate HDR brachyt...
Authors: Mingli Chen, Xuejun Gu, Hao Jiang, Mahdieh Kazemimoghadam, Weiguo Lu, Qingying Wang, Kangning Zhang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Department of Radiation Oncology, Stanford University School of Medicine
Abstract Preview: Purpose:
This work demonstrates how existing software, when creatively adapted, can address a wide range of clinical challenges. By focusing on data exploration and application-specific modificatio...
Authors: Freeman Jin, Paul J. Keall, Alistair MacDonald, Adam Mylonas, Chandrima Sengupta
Affiliation: Image X Institute, Faculty of Medicine and Health, University of Sydney, Image X Institute, Faculty of Medicine and Health, The University of Sydney, Image X Institute, School of Health Sciences, University of Sydney
Abstract Preview: Purpose: During radiation therapy, tumours in the prostate may move from the planned treatment position, leading to significant dose deviations above clinical tolerances Surveys have indicated the nee...
Authors: Laura I. Cervino, Wendy B. Harris, Paulo Quintero, Hao Zhang
Affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center
Abstract Preview: Purpose: To evaluate the impact of the prediction uncertainty from CBCT-based synthetic CT (sCT) generation in abdominal adaptive radiotherapy.
Methods: CT and CBCT images from 65 abdominal pat...
Authors: Mingli Chen, Xuejun Gu, Hao Jiang, Mahdieh Kazemimoghadam, Weiguo Lu, Qingying Wang, Kangning Zhang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Department of Radiation Oncology, Stanford University School of Medicine
Abstract Preview: Purpose:
Converting MR images to synthetic CT (MR2sCT) is highly desirable as it streamlines the radiotherapy treatment planning workflow. This approach leverages the superior soft tissue visibilit...